We're creating a State of BI report and are interviewing data/analytics/BI leaders from large enterprises to explore BI's current state, long-term strategies, and AI's impact.
Have you ever heard about the buzzwords "business analytics" or "data analytics"? These skills are most in-demand in today's data world. Data analytics is not a new skill to the market, and this dates back to the days of Microsoft Excel. Analysts have been doing data analysis with interactive charts and figures to impress the non-technical stakeholders to provide advanced insights and strategize their business. During the last two decades, the data has been flowing in abundance to the companies, and hence the new modern, advanced, interactive, and more accessible tool was required, which gave birth to tools like Power BI.
Power BI is a unified, scalable platform for self-service and enterprise business intelligence (BI) that's easy to use. It helps you gain deeper data insight by connecting to and visualizing any data. It can handle a wide variety of data types, and massive data amounts easily. Flexible features like combining and reshaping data from different data sources, then creating reports and dashboards is a game-changer. These interactive reports are used to make essential decisions that directly impact business. When we talk about sensitive business data, privacy is always a concern. Power BI helps protect your data across Power BI reports, dashboards, and data sets with continuous protection that keeps working even when shared outside your organization or exported to other formats such as Excel, PowerPoint, and PDF. Microsoft products are often some of the most trusted tools in the industry.
Aside from the numerous benefits provided by Power BI, security and various sorts of Big Data connectors are what distinguish Power BI as a premier analytics platform.
Microsoft is well known for providing top-tier security across all of its products, including Outlook, Teams, MS Office, and Power BI is no exception. The Power BI service is built on Azure, which is Microsoft’s cloud computing infrastructure and platform. The Power BI service architecture is based on two clusters – the Web Front End (WFE) cluster and the Back-End cluster. The WFE cluster manages the initial connection and authentication to the Power BI service, and once authenticated, the Back-End handles all subsequent user interactions. Power BI uses Azure Active Directory (AAD) to store and manage user identities and store data and metadata using Azure BLOB and Azure SQL Database, respectively. Power BI uses two primary repositories for storing and managing data: data that is uploaded from users is typically sent to Azure Blob Storage, and all metadata, as well as artifacts for the system itself, are stored in Azure SQL Database.[1]
Power BI can connect to a wide variety of different data sources. Whether that's Excel, SQL, relational databases, cloud sources or services that you use, like Salesforce or QuickBooks, or maybe it's just an API that you want to get data, or even a webpage. To collect and analyze massive amounts of data, Microsoft Power BI can be readily linked with various Big Data sources. Power BI comes with Power Query and Power Map, which can be readily coupled with Big Data analyses utilizing the Office 365 suite.
Documentation is a vital element of any process in general, and the same is valid for data analysis. The availability of documentation aids in keeping track of all the dashboards. Its primary focus is development, maintenance, and knowledge transmission to other analysts. Power BI effectively does not allow for documentation. This makes it harder to retrieve information, makes it difficult for new users to understand the product quickly, complicates the product, and raises support expenses.
Dashboards can be accessed by any user who has access to insights, but they are only owned by limited individuals who created them. A dashboard owner can then share their dashboard(s) with any or all team members. Many times, the analysis process will be slowed because the owners of the dashboard are unknown. Consider the following scenario: an analyst needs to make a limited activity on the dashboard that requires admin access or approval. Due to a lack of ownership information, the analyst must contact up and down the chain of command to locate the owner and request authorization.
Projects and dashboards are scattered among datasets and tools in any business. It is difficult to find information with only Power BI. Duplication occurs due to a lack of centralized search. Consider the possibility that if you cannot identify a dataset quickly, you will end up importing the same dataset again, resulting in duplication. It is also challenging to identify dashboards when a team has accumulated many of them over the years.
Metadata is information that describes other data. Metadata is generated when data is created, acquired, added to, deleted from, or altered. The purpose of centralized metadata is to make it easier for a person or program to locate information about one or many specific data assets. As Power BI does not give a consistent manner of searching, it is more difficult to access information on dashboards. The analyst's life will be made difficult if they do not know crucial facts, such as who utilizes the dashboard or why it was built.
In a nutshell, handling large amounts of data, information, and dashboards is a nightmare, and Power BI provides little assistance. As a result, the environment becomes dirty and unmanaged. It creates a problem with workspace management for the analysts. Keeping all workspace data in one place without a clear indication of which is ready for production and requires additional development is unpleasant, especially when you need to retrieve something quickly.
Datalogz has identified these difficulties that developers and analysts confront and developed intelligent data, workspace, and dashboard management solutions in response.
Datalogz is a one-stop solution for all your data and workspace management needs. You can seamlessly integrate data from different sources such as dbt, Snowflake, Postgres, etc., upon which you have built Power BI dashboards. Users can readily document on the fly, making it easier to rapidly grasp the dashboards.
Users can also tag which work is ready for production, which requires further work, and which can simply be ignored. Unlike Power BI, Datalogz can save dataset metadata such as column descriptions, changed date-time, and upstream dataflow, which is regularly updated to offer you with a better, more organized, and optimal view of all data and workspaces.
Datalogz supports Power BI's newest Metadata API connection, allowing you to link your Power BI assets into your Datalogz environment instantly. When Power BI creates reports, it also creates dataset metadata in the corresponding PBIX and PBIT files.[2] Datalogz connects to these files using the OAuth2.0 flow. Datalogz scans the workspace and retrieves the desired data using the Admin - WorkspaceInfo GetScanResult API. This API results from the initiated request given that the user must have administrator rights or authenticate using a service principal ensuring Power BI promised privacy and security into Datalogz. The real power comes when Datalogz can scan numerous workspaces in the background at the same time, allowing for immediate data and report the discovery.
The display name, URL, titles, and dashboard owner information are all received timely and displayed in your Datalogz window. The user can also change the status of several dashboards, such as ready for production, in-process, and so on.
You can finally put an end to dashboard and data misunderstandings with Datalogz. Users no longer have to guess what a report is about or how to use it. Users may discover, document, share, and comprehend any report directly in Datalogz.
Learn more about our product and its various applications. We have an extremely dedicated and smart working team. Keep up with the latest blogs.
